visualising variables – validly! damien jolley monash institute of health services research monash...

Visualising Variables – Validly!

Damien JolleyMonash Institute of Health Services

ResearchMonash University

AHMRC Posters

Turning Point Alcohol & Drug Centre

31 May 2005

Download slides from: http://www.jolley.com.au

Average daily retail petrol price,Melbourne, 29 April-28 May 2005

Wed Wed WedMon Mon MonMon Th

Source: http://www.accc.gov.au, 29 May 2005

http://www.accc.gov.au/


Download slides from: http://www.jolley.com.auAverage daily retail petrol price,Melbourne, Oct-Nov 2002

Th Th TuSat Sat Fri SatTh

Source: http://www.accc.gov.au, 21 Nov 2002




Price & pattern has changed…

90

100

110

90

100

110

01/05 08/05 15/05 22/05

01/05 08/05 15/05 22/05 01/05 08/05 15/05 22/05

Melbourne Sydney Adelaide

Brisbane Perthpric

e

dateGraphs by city

Daily average petrol prices (c/litre) in

selected Australian cities,

May 2005

Source: http://www.accc.gov.au


Obvious fact #1:

Graphs can communicate data:

quickly

accurately

powerfully

efficiently

Download slides from: http://www.jolley.com.au“Only 50% of American 17-year-olds can identify information in a graph”*

Source: Wainer H. Understanding graphs and tables. Educational

Researcher 1992; 21:14-23

* US National Assessment of

Educational Progress,June 1990


Whose fault?

Source: Wainer H. Understanding graphs and tables. Educational

Researcher 1992; 21:14-23

0

20

40

60

80

1970 1980 1990 2000

Year

Qu

ad

rilli

on

BT

Us

Petroleum

Nuclear power

Natural gas

Coal

Hydropower

“Like characterising someone’s ability to read by asking questions about a passage full of spelling

and grammatical errors. What are we really testing?”

Draw

n using MS Excel ‘XY-chart’


Obvious fact #2:

Bad graphs can hinder communication


Less obvious facts #3, #4, #5:

What characterises a “good” graph?

What are the characteristics of a “bad” graph?

What software to use? How to use it?


Howie’s Helpful Hintsfor bad graph displays

Ten useful pointers to help you create uninformative, difficult-to-read scientific graphs

Adapted from:Wainer H. (1997) Visual Revelations. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers


Steps for better graphs

1. Identify direction of effect In almost all cases, the cause or predictor

variable should be horizontal (X) Effect or outcome variable is best vertical (Y)

2. Identify the levels of measurement Nominal, ordinal or quantitative are different!

3. Think of visual perception guides Columns or dots? Lines or scatterplot?

4. Minimise guides and non-data Grid lines, tick marks, legends are non-data


Cause (X) and effect (Y)

Figure 16

Standard deviation of batting averages for all full-time players by year for the first 100 years of professional baseball. Note the regular decline.*

Standard deviation

Tim

e

Source:Gould, Stephen Jay. Full House: The Spread of Excellence from

Plato to Darwin. Random House, 1997.cited: http://www.math.yorku.ca/SCS/Gallery/, 24 Nov 2002* My emphasis

Standard deviation

Time

http://www.math.yorku.ca/SCS/Gallery/



Source:

Killias M. International correlations

between gun ownership and rates

of homicide and suicide.

Can Med Assoc J 1993; 148: 1721-5

% of households owning guns

Rat

e o

f h

om

icid

e w

ith

a g

un

(p

er m

illio

n p

er y

ear)

10 20 30 40

1

5

10

50 USA

Norway

Canada

France

FinlandBelgium

Australia

SpainSwitzerland

Netherlands

West Germany

Scotland

England & Wales

Drawn using S-plus


Levels of Measurement

The right display for a variable depends on its level of measurement

For univariate graphs, qualitative barplot ordinal column chart quantitative boxplot

or histogram

For bivariate graphs, X ordinal, Y binary

connected percents X & Y both quantitative

scatterplot X categorical, Y quant

box plots

Binary eg gender, death, pregnant

Categorical Qualitative

eg race, political party, religion Diverging

eg change (-ve to +ve) Ordinal

eg rating scale, skin type, colour Quantitative

Interval only differences matter, eg BP, IQ

Ratio absolute zero, ratios matter,

eg weight, height, volume

Source:Lewis S, Mason C, Srna J.

Carbon monoxide exposure in blast furnace workers.

Aust J Public Health. 1992 Sep;16(3):262-8.

Ordinal variable, but categories

mixed

Outcome is COHb%, but drawn on X


An alternative display . . .

Smokers

0%

5%

10%

Blast Furnace Exposure

None Low High

Non-smokers

0%

5%

10%

Blast Furnace Exposure

CO

Lev

el in

blo

od

(%

)

None Low High

Area of circles proportional to nPredictor variable

Ou

tcom

e va

riab

le

Drawn using MS Excel ‘bubble plot’


Principles of visual perception

WS Cleveland much work in psycho-

physics of human visual understanding

Tells us: hierarchy of visual

quantitative perception

patterns and shade can cause vibration

graphs can shrink with almost no loss of information

Source: Cleveland WS. The Elements of Graphing Data. Monterey: Wadsworth, 1985.


Ubiquitous column charts

Source: Jamrozik K, SpencerCA, et al. Does the Mediterranean paradox extend to abdominal aortic aneurism? Int J Epidemiol 2001; 30(5): 1071


A dotchart version…

MediterraneanNetherlands

All otherOther N Europe

AustraliaScotland

Full fat milk

50 60 70 80

Adds salt

50 60 70 80

Meat 3+ weekly

50 60 70 80

Fish 1+ weekly

50 60 70 80

Percent

Drawn using S-plus “Trellis” graphics

Moiré vibration

is easy with

a computer !!!


Moiré vibration

Vibration is maximised with lines of equal separation

This is common in scientific column charts

cited in Tufte E. The Visual Display of Quantitative Information.


Minimise non-data ink

Non-data ink includes tick marks, grid lines, background, legend

Explanation of error bars, P-values can be included in caption or in text

Greeks in Australia

Swedes in Sweden

Japanese in Japan

Anglo-Celts in Australia

Greeks in Greece

0.10 0.25 0.50 0.75 1.00Relative mortality rate (all causes)

Note the exception for X-Y orientation: because predictor is qualitative (unordered)


Software for scientific graphics

Dedicated programs – thousands!

DeltaGraph (SPSS)

Prism ViSta

Business graphics MS Excel many other

spreadsheet programs

Graphics in statistical packages

Stata simple, powerful

S-Plus, R powerful, difficult

SPSS interactive graphics

easy, expensive Systat

good reputation SAS GRAPH language

expensive, powerful

Advice: Avoid “default” choice in all programs (almost always wrong).Avoid programs with “Chart Type” menus – wrong approach.


Graph formats

Object-oriented lines, shapes, etc

can be identified within graph

each object has attributes (eg size, colour, font)

editable using selection and “grouping”

Common formats: Postscript (ps,eps) Windows metafile

(wmf,emf)

Bit-mapped image exists as a

collection of pixels each pixel is light or

dark, coloured can edit only pixels not

objects often “compressed” to

save disk space, bandwidth

Common formats graphics interchange

(gif) Windows bitmap (bmp) JPEG interchange (jpg)

Advice: Use WMF format where possible. Paste WMF into PowerPoint, “ungroup”, then edit objects for publication quality.


References, further reading

Tufte ER.

The Visual Display of Quantitative Information

Cheshire, CT: Graphics Press 2001

www.edwardtufte.com

Cleveland WS.

Visualizing Data

Summit NJ: Hobart Press, 1993

Wainer H.

Visual Revelations. Graphical Tales of Fate and Deception from Napoleon Bonaparte to Ross Perot

Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. 1997

www.erlbaum.com

Wilkinson L.

The Grammar of Graphics

New York: Springer Verlag, 1999


Summary

Howie’s Helpful Hints for bad graphs: Don’t show the data Show the data inaccurately Obfuscate the data

Steps for better graphs: Identify direction of cause & effect Exploit levels of measurement Accommodate visual perception principles Minimise non-data ink

Don’t use Excel unless you have to And if you have to, don’t use the default

chart!

visualising variables – validly! damien jolley monash institute of health services research monash...

Documents

download slides

x slide

volume slide

communication slide

splus slide

nondata slide

ordinal s

understanding graphs